Thread

1. kamranjon · 1789680375 · focus · HN ↗

   Love this for the folks with 16gb graphics cards - 3.8 27b has been incredible but not quite runnable on anything less than 32gb - will try loading this up on my 16gb intel b50 and see how it goes - not sure these quants can be accelerated by the XPU cores yet but maybe in time!

   1. kadoban · 1789681996 · focus · HN ↗

      You can run the ~4 bit quant(s) on 24gb, if you're not _too_ picky on context size.

      This will hopefully be better, though it'd be a _very_ surprising increase in performace at the size they say. Would love to see more about how it benchmarks.

      1. spijdar · 1789682410 · focus · HN ↗

         I run Unsloth's UD-Q4_K_S on 20 GB of VRAM (RX 7900 XT) and I get ~90k tokens of context without quantizing KV cache. With 8-bit quantization, I get about a 134k token context window. That's with only one slot, but for me, it works pretty darn well, with 20-35 tok/s depending on how full that window is.

         1. orsorna · 1789690726 · focus · HN ↗

            7900 XT is a sleeper card. When I initially bought it, it was priced at the lowest wattage per $ per GB VRAM (not normalized for token speeds...) Although I ended up swapping for the XTX because that 4GB means everything in just increasing the context window. At 8bit KV my window is over 200k, and although qwen3.8 loves vomiting out tokens as part of its reasoning chain I trust it enough to get assigned tasks done eventually, which I could not say of any model before its release.

            1. kadoban · 1789702964 · focus · HN ↗

               How has software/driver support been? I got burned hard by AMD last generation or the one before. Things smoother now, or do you have to baby it like hell and pick and choose software that works?

               1. orsorna · 1789704232 · focus · HN ↗

                  I don't do anything fancier than inference, and I only use llama.cpp, which supports rOCM. I've had few issues; most GGUFs I download work right out of the box. Nearly any popular model has a quant that just works. But as you can see I don't use my GPU for anything weird or nonstandard.
      2. lta · 1789695376 · focus · HN ↗

         I'm doing the same with a context of about 128-150k Surprisingly, I get subjectively better results with Unsloth's 3 bit quants (UD-Q3-XL something), than their 4 bit quants (S or M)
      3. redox99 · 1789699167 · focus · HN ↗

         You can trivially run 131k on 24GB 4bit, and there are repos with tweaks that allow you to get the full 262k but idk if there's degradation with their approach.

         1. Zambyte · 1789734799 · focus · HN ↗

            How? I'm running 4bit with a q8 kv on a 24gb card, and I'm not able to get 100k out of it. I use a context size of 90k.

            1. [deleted] · 1790012305 · focus · HN ↗

               [deleted]

   2. djkoolaide · 1789695087 · focus · HN ↗

      Tried it today on a B70 and couldn't get anything usable out of it.

      Prism's llama.cpp fork only has the kernels for CUDA, CPU and Vulkan. No SYCL at all :(

   3. kennywinker · 1789708968 · focus · HN ↗

      I run qwen 27b on an old-ass 16gb gpu. It’s very possible using unsloth 2bit and 3bit quants, tho there are a bunch of interesting quants that let you run closer to 4bit on 16gb. This article that’s currently also on the front page mentions a bunch of them while discussing their own quant <a href="https:&#x2F;&#x2F;byteshape.com&#x2F;blogs&#x2F;Qwen3.8-27B&#x2F;" rel="nofollow">https:&#x2F;&#x2F;byteshape.com&#x2F;blogs&#x2F;Qwen3.8-27B&#x2F;

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